investigation_log_experiment

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It does not disclose behavioral traits like whether the tool is append-only, requires an existing investigation, or any permissions/side effects. For a logging tool, this is a significant gap.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence that is front-loaded and efficient. Every word adds value, with no waste or redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has 5 parameters (including a nested results object) and no output schema, the description is too minimal. It does not explain the return value, how results should be structured, or the relationship to other investigation tools. This leaves the agent with incomplete guidance.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, but the description lists the parameter names in parentheses (hypothesis, methodology, results, conclusion), adding meaning beyond just the schema property names. However, it does not provide details on format, constraints, or examples, which is needed for 5 parameters including a nested object.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action 'log a structured experiment' and lists the components (hypothesis, methodology, results, conclusion). This distinguishes it from sibling tools like investigation_add which likely adds a different entity, and investigation_list_experiments which lists experiments.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description does not explicitly state when to use this tool versus alternatives. It implies usage for logging experiments but lacks explicit context or when-not conditions. For a logging tool, basic usage context would improve selection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.